To compute the curviness of stripe–interstripe interfaces, the first step is extracting the boundaries of our clusters of \(X^\text{d}\)  cells from Step 4. There are many ways to do this: for example, focusing on one interstripe at a time, McGuirl et al. [1]} discretized space and then found the cells with the maximum and minimum \(y\) -coordinates in each grid step. Taken across all the grid positions, these highest and lowest cells trace out the upper and lower boundaries of an interstripe [1]}. Owen et al. [3]} developed an algorithm for extracting the upper and lower boundaries of the center interstripe in the domain. This approach [3]} involves expressing the lattice of \(X^\text{d}\)  cells as a binary image and applying the built-in MATLAB function \(\emph {bwmorph}\)  to clean the data and bridge small gaps in interstripes. Owen et al. then identify the height of the pixels associated with the upper and lower boundaries of the center interstripe; their methods [3]} focus on interstripe curviness as a whole—rather than stripe–interstripe boundary curviness—so they later take the mean of these two curves and apply a smoothing algorithm. Importantly, both approaches [1]}, [3]} rely on discretizing space: the lattice structure of the CA [3]} lends itself to image-processing techniques for binary images, and McGuirl et al. [1]} introduce a spatial discretization to extract interstripe boundaries in the ABM.
